Starling LM 7B Alpha
Starling LM 7B Alpha has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating general LLM work
- Workloads that can use a 8k context window
Do not use it for
- Cost-sensitive launches that need sourced token pricing
- Vision or document-understanding workloads
- Strict JSON or tool-calling flows
- Family
- Starling Alpha
- Released
- 2024-02-05
- Context
- 8k
- Parameters
- 7B
- Architecture
- Decoder Only
- Specialization
- general
- Training
- finetuned
No tracked provider token pricing is available yet.
About
Starling-LM-7B-alpha is an advanced large language model developed by Berkeley NEST, utilizing the Openchat 3.5 framework, which builds on Mistral-7B-v0.1. It incorporates Reinforcement Learning from AI Feedback (RLAIF) and is fine-tuned with the Nectar dataset, comprising 183,000 chat prompts and 3.8 million pairwise comparisons. It excels in conversational tasks, scoring 8.09 on the MT Bench benchmark, surpassing most models except OpenAI's GPT-4 and GPT-4 Turbo. Despite its prowess in dialogue, content generation, and question answering, it struggles with reasoning and mathematics and sometimes produces verbose responses or is vulnerable to jailbreaking prompts, highlighting areas for potential enhancement 1 2 3.
Starling LM 7B Alpha is a model in the Starling Alpha family. The structured metadata tracks a 8k-token context window. No headline benchmark score is tracked for Starling LM 7B Alpha yet.
Top use-case fit
No primary decision-task fit is mapped for this model yet.
Provider price ladder
No tracked provider token pricing is available for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Coding
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.